Utility maximization in peer-to-peer systems
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
EURASIP Journal on Wireless Communications and Networking
Rate control for network coding based multicast: a hierarchical decomposition approach
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
Rate Control, Routing Algorithm and Scheduling for Multicast with Network Coding in Ad Hoc Networks
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Pruning network coding traffic by network coding: a new class of max-flow algorithms
IEEE Transactions on Information Theory
Layering as optimization decomposition: questions and answers
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Rate control with pairwise intersession network coding
IEEE/ACM Transactions on Networking (TON)
Cross-layer designs in coded wireless fading networks with multicast
IEEE/ACM Transactions on Networking (TON)
Distributed network coding-based opportunistic routing for multicast
Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing
Utility maximization in peer-to-peer systems with applications to video conferencing
IEEE/ACM Transactions on Networking (TON)
Scheduling for network-coded multicast
IEEE/ACM Transactions on Networking (TON)
Minimum cost multiple multicast network coding with quantized rates
Computer Networks: The International Journal of Computer and Telecommunications Networking
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One central issue in practically deploying network coding is the adaptive and economic allocation of network resource. We cast this as an optimization, where the net-utility-the difference between a utility derived from the attainable multicast throughput and the total cost of resource provisioning-is maximized. By employing the MAX of flows characterization of the admissible rate region for multicasting, this paper gives a novel reformulation of the optimization problem, which has a separable structure. The Lagrangian relaxation method is applied to decompose the problem into subproblems involving one destination each. Our specific formulation of the primal problem results in two key properties. First, the resulting subproblem after decomposition amounts to the problem of finding a shortest path from the source to each destination. Second, assuming the net-utility function is strictly concave, our proposed method enables a near-optimal primal variable to be uniquely recovered from a near-optimal dual variable. A numerical robustness analysis of the primal recovery method is also conducted. For ill-conditioned problems that arise, for instance, when the cost functions are linear, we propose to use the proximal method, which solves a sequence of well-conditioned problems obtained from the original problem by adding quadratic regularization terms. Furthermore, the simulation results confirm the numerical robustness of the proposed algorithms. Finally, the proximal method and the dual subgradient method can be naturally extended to provide an effective solution for applications with multiple multicast sessions